Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing, China.
Informatics Research Centre, Henley Business School, University of Reading, Reading, UK.
Health Info Libr J. 2020 Mar;37(1):48-59. doi: 10.1111/hir.12253. Epub 2019 May 14.
Online health communities (OHCs) experience difficulties in utilising patient reported posts to fulfil the information needs of online patients concerning health related issues.
We aim to propose a comprehensive method that leverages medical domain knowledge to extract useful information from posts to fulfil information needs of online patients.
A knowledge representation framework based on authoritative knowledge sources in the medical field for the OHC is proposed. On the basis of the framework, a health related information extraction process for analysing the posts in the OHC is proposed. Then, knowledge support rate (KSR) and effective information rate (EIR) are introduced as metrics to evaluate changes in knowledge extracted from the knowledge sources in terms of fulfilling the information needs of patients in the OHC.
On the basis of a data set with 372 343 posts in an OHC, experimental results indicate that our method effectively extracts relevant knowledge for online patients. Moreover, KSR and EIR are feasible metrics of changes in knowledge in terms of fulfilling the information needs.
The OHCs effectively fulfil the information needs of patients by utilising authoritative domain knowledge in the medical field. Knowledge based services for online patients facilitate an intelligent OHC in the future.
在线健康社区(OHC)在利用患者报告的帖子来满足在线患者对健康相关问题的信息需求方面存在困难。
我们旨在提出一种综合方法,利用医学领域的知识从帖子中提取有用信息,以满足在线患者的信息需求。
提出了一个基于医学领域权威知识源的 OHC 知识表示框架。在此框架基础上,提出了一种用于分析 OHC 中帖子的健康相关信息提取过程。然后,引入知识支持率(KSR)和有效信息率(EIR)作为度量标准,以评估从知识源中提取的知识在满足 OHC 中患者信息需求方面的变化。
基于一个包含 372343 个帖子的 OHC 数据集,实验结果表明我们的方法有效地提取了在线患者的相关知识。此外,KSR 和 EIR 是衡量满足信息需求的知识变化的可行指标。
OHC 通过利用医学领域的权威领域知识,有效地满足了患者的信息需求。面向在线患者的基于知识的服务将促进未来智能 OHC 的发展。